NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

DeepSeek Coder V2 236B (MoE) on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

DeepSeek Coder V2 236B (MoE) at AWQ 4-bit needs 129.8 GB for its weights and 142.4 GB in total at 8K context with 8 concurrent users. That means it needs 2× H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
142.4 GB needed / 160 GB available
Decode speed
282.0
tok/s per stream
Aggregate
6769
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2052
2× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× H10080 GB142.4 GB0.0000.000——✗
2× H100160 GB142.4 GB282.06769381.1 ms$0.2052✓
4× H100320 GB142.4 GB564.127076205.5 ms$0.1026✓
8× H100640 GB142.4 GB1128108303117.8 ms$0.0513✓
Concurrency vs context length
On 2× H100 SXM5 80GB at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K38807.5 MB✓ fits
8K191.58 GB✓ fits
33K46.31 GB✓ fits
66K212.62 GB✓ fits
131K125.23 GB✓ fits
Other GPUs that run DeepSeek Coder V2 236B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB8×57934$0.0192
L40S 48GB4×6983$0.1114
A100 80GB SXM42×4041$0.1650
AMD MI300X 192GB1×3150$0.2205
H200 SXM5 141GB2×9699$0.2291
AMD MI325X 288GB1×3566$0.2727
Frequently asked questions

Can DeepSeek Coder V2 236B (MoE) run on H100 SXM5 80GB?

Yes, but it needs 2× H100 SXM5 80GB. At AWQ 4-bit the total footprint is 142.4 GB (129.8 GB of weights plus 12.6 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is DeepSeek Coder V2 236B (MoE) on H100 SXM5 80GB?

282.0 tokens/sec per stream and 6769 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 381.1 ms, and inter-token latency is 3.5 ms.

What does DeepSeek Coder V2 236B (MoE) on H100 SXM5 80GB cost per million tokens?

$0.2052 per 1M output tokens at 2× H100 SXM5 80GB ($2.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does DeepSeek Coder V2 236B (MoE) need on H100 SXM5 80GB?

129.8 GB for the weights at AWQ 4-bit, plus 12.6 GB of KV cache for 8 concurrent requests at 8K context — 142.4 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for DeepSeek Coder V2 236B (MoE)?

It works. Compare $0.2052 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 5090 32GB at $0.0192, and the fastest is 4× Cerebras CS-3 (WSE-3) at 161646 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator